app.py

# Import required libraries
import os
import pandas as pd
import streamlit as st
# from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification

from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import requests
import json

# Configure Hugging Face API token securely
api_key = os.getenv("HF_API_KEY")

# Load the CSV dataset
try:
    data = pd.read_csv('genetic-Final.csv')  # Ensure the dataset filename is correct
except FileNotFoundError:
    st.error("Dataset file not found. Please upload it to this directory.")

# Load DistilBERT Tokenizer and Model
# tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
# model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')

# Load DistilBERT tokenizer and model (without classification layer)
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
model = DistilBertModel.from_pretrained("distilbert-base-uncased")

query = "What is fructose-1,6-bisphosphatase deficiency?"

# Tokenize input
inputs = tokenizer(query, return_tensors="pt", padding=True, truncation=True)

# Get model output (embeddings)
with torch.no_grad():
    outputs = model(**inputs)

# Extract embeddings (last hidden state)
embeddings = outputs.last_hidden_state.mean(dim=1)  # Averaging over token embeddings

# Use the embeddings for further processing or retrieval
print(embeddings)


# Preprocessing the dataset (if needed)
if 'combined_description' not in data.columns:
    data['combined_description'] = (
        data['Symptoms'].fillna('') + " " +
        data['Severity Level'].fillna('') + " " +
        data['Risk Assessment'].fillna('') + " " +
        data['Treatment Options'].fillna('') + " " +
        data['Suggested Medical Tests'].fillna('') + " " +
        data['Minimum Values for Medical Tests'].fillna('') + " " +
        data['Emergency Treatment'].fillna('')
    )

# Initialize Sentence Transformer model for RAG-based retrieval
retriever_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Define a function to get embeddings using DistilBERT
def generate_embedding(description):
    if description:
        inputs = tokenizer(description, return_tensors='pt', truncation=True, padding=True, max_length=512)
        outputs = model(**inputs)
        return outputs.logits.detach().numpy().flatten()
    else:
        return []

# Generate embeddings for the combined description
if 'embeddings' not in data.columns:
    data['embeddings'] = data['combined_description'].apply(generate_embedding)

# Function to retrieve relevant information based on user query
def get_relevant_info(query, top_k=3):
    query_embedding = retriever_model.encode(query)
    similarities = [util.cos_sim(query_embedding, doc_emb)[0][0].item() for doc_emb in data['embeddings']]
    top_indices = sorted(range(len(similarities)), key=lambda i: similarities[i], reverse=True)[:top_k]
    return data.iloc[top_indices]

# Function to generate response using DistilBERT (integrating with the model)
def generate_response(input_text, relevant_info):
    # Concatenate the relevant information as context for the model
    context = "\n".join(relevant_info['combined_description'].tolist())
    input_with_context = f"Context: {context}\n\nUser Query: {input_text}"

    # Simple logic for generating a response using DistilBERT-based model
    inputs = tokenizer(input_with_context, return_tensors='pt', truncation=True, padding=True, max_length=512)
    outputs = model(**inputs)
    logits = outputs.logits.detach().numpy().flatten()
    response = tokenizer.decode(logits.argmax(), skip_special_tokens=True)
    
    return response

# Streamlit UI for the Chatbot
def main():
    st.title("Medical Report and Analysis Chatbot")
    st.sidebar.header("Upload Medical Report or Enter Query")

    # Text input for user queries
    user_query = st.sidebar.text_input("Type your question or query")

    # File uploader for medical report
    uploaded_file = st.sidebar.file_uploader("Upload a medical report (optional)", type=["txt", "pdf", "csv"])

    # Process the query if provided
    if user_query:
        st.write("### Query Response:")

        # Retrieve relevant information from dataset
        relevant_info = get_relevant_info(user_query)
        st.write("#### Relevant Medical Information:")
        for i, row in relevant_info.iterrows():
            st.write(f"- {row['combined_description']}")  # Adjust to show meaningful info

        # Generate a response from DistilBERT model
        response = generate_response(user_query, relevant_info)
        st.write("#### Model's Response:")
        st.write(response)

    # Process the uploaded file (if any)
    if uploaded_file:
        # Display analysis of the uploaded report file (process based on file type)
        st.write("### Uploaded Report Analysis:")
        report_text = "Extracted report content here"  # Placeholder for file processing logic
        st.write(report_text)

if __name__ == "__main__":
    main()